What is the difference between a technical audit and technical due diligence?

A technical audit is a forensic examination that checks code quality against a standard, while technical due diligence is a commercial risk assessment that evaluates whether the technology can support the financial assumptions of a deal. Audits tell you the code is messy; diligence tells you how that mess translates into money and schedule risk.

Why has AI-generated code become a primary risk indicator in modern due diligence?

AI-generated code often results in "vibe-coded" systems that look correct but fail under adversarial input or scale, creating hidden technical debt. Reviewers now prioritize critical paths such as authentication, payments, and AI agents with write access because these areas are most vulnerable to AI hallucinations and business-critical failures.

Which manual checks deliver the most value during a short-term due diligence engagement?

High-value manual checks include a ten-minute full restore test, the "Bus Factor" interview with the lead engineer, the provisioning race to rebuild the production environment, a logic trace of a real request through the stack, IP assignment review for contractor copyrights, and load testing at twice peak traffic. These activities expose risks that automated scanners cannot detect.

How are technical findings translated into financial impact for the transaction?

Findings are mapped to severity levels (critical, major, or minor) with each level linked to a specific deal impact such as price adjustments, escrow, or post-close remediation budgets. The estimate is expressed in engineer-weeks, turning technical debt into a concrete financial liability that can be negotiated in the purchase agreement.

Technical Due Diligence Checklist: What Changes in Practice

A technical due diligence checklist is often mistaken for a technical audit, but the two serve different masters. An audit is a forensic examination of quality (a quest for "correctness" against a standard) whereas technical due diligence is a commercial risk assessment designed to determine if the technology can support the financial assumptions of a deal. While an audit tells you the code is messy, diligence tells you that the mess will cost $150,000 and four months of delayed roadmap to fix.

The transition from a static list of questions to a practical assessment requires moving from a binary "yes/no" mindset to a weighted risk model. In professional practice, the goal is not to find every bug, but to identify the material risks that should either change the purchase price or serve as a condition for closing.

The Shift in Weighting: The AI Variable

The traditional checklist has not changed in its core categories (code, security, infrastructure, and team) but the weighting of those categories has shifted violently. In previous years, "code provenance" was a minor check for open-source compliance. Now, it is a primary risk indicator.

Diligence is the only way to ensure you aren't paying full price for a system that cannot scale.

As highlighted by DevEntia, a significant portion of modern codebases are now generated by AI, often by founders who cannot explain the resulting logic. This introduces a specific type of technical debt: "vibe-coded" systems that look functional and well-formatted but fail under adversarial input or scale. When reviewing a modern target, the manual review phase must prioritise the "critical paths" (authentication, payments, and any AI agents with write access) because these are where AI-generated hallucinations create catastrophic business risk.

From Evidence to Remediation

A checklist is a tool for gathering evidence, but evidence without a price tag is useless to a deal team. The practice of high-end diligence involves mapping every failing check to a remediation estimate in engineer-weeks.

Finding Severity	Typical Definition	Deal Impact	Remediation Action
Critical	Account ownership in personal names; UI-only auth	Price adjustment or Escrow	Fix as a closing condition
Major	Untested backups; no CI/CD pipeline	Post-close 90-day plan	Budget for remediation
Minor	Dead code; inconsistent naming	Noted in report	Fold into general maintenance

The most dangerous findings are those that are "cheap" to describe but "expensive" to fix. For example, finding that a target company uses a monolith instead of microservices is rarely a deal-breaker. However, finding that the cloud accounts are owned by a former agency's personal email is a critical risk; it is a simple administrative detail that can lead to a total lockout after a dispute.

The High-Value Manual Checks

Automated scanners are essential for catching CVEs and secret leaks, but the most material risks are found in the gaps between the tools. A rigorous process focuses on "proof of life" tests.

One of the highest-value activities in a two-week window is the restore test. Many companies have backup scripts that run daily, but few have tested a full restore into a clean environment. As DevEntia notes, a ten-minute restore test is often the highest-value hour of the entire review because it exposes silent failures that have persisted for months.

Other non-automatable checks include:

The "Bus Factor" Interview: Asking the lead engineer to explain the most complex module. Hesitation during this interview is data.
The Provisioning Race: Attempting to build the production environment from the repository. If the deploy depends on a specific laptop or undocumented manual steps, the build is not reproducible.
The Logic Trace: Manually tracing one real request from the UI through the API, database, and third-party integrations to verify that the described architecture matches reality.
IP Assignment Review: Verifying that every contractor who touched the core codebase signed a copyright assignment.
Load Testing: Running the system at twice its current peak to find the point of collapse.

Diligence is the only way to ensure you aren't paying full price for a system that cannot scale.

Integrating Findings into the Transaction

The final output of a technical due diligence checklist must be a decision-oriented report. This connects the technical reality to the IT Due Diligence Process: What Good Looks Like by translating "technical debt" into "financial liability."

A professional report avoids vague terms like "moderate risk" and instead uses a rubric to categorize the target. A "Sound" profile has zero criticals and requires minimal remediation. An "Investable with Conditions" profile may have one or two criticals that must be fixed before the wire transfer. A "Rescue Required" profile is one where the cost to fix the architecture exceeds the projected value of the roadmap.

To move from a simple list of flaws to a strategic assessment, one must employ a structured framework. The Dextra Labs process details a 7-phase approach (from scope and discovery to final risk analysis) ensuring that the review is aligned with the specific investment thesis rather than a generic template.

Furthermore, the operational health of the engineering team must be quantified. GainHQ examines the use of DORA metrics (deployment frequency, lead time for changes, change failure rate, and MTTR) to distinguish between a team that simply ships features and one that maintains a sustainable, high-velocity delivery engine. Without these metrics, a buyer cannot know if the "velocity" claimed by the seller is real or merely a result of ignoring quality and security.

Ultimately, the process should move linearly: evidence leads to risk, risk leads to business impact, and business impact leads to a remediation cost that informs the final deal decision.

Sources

Technical Due Diligence Checklist 2026 | DevEntia: covers the impact of AI-generated code and a 30-point risk-rated checklist.
Technical Due Diligence Process and Checklist for Buyers [2026] (dextralabs.com): details the 7-phase process from discovery to risk analysis.
Technical Due Diligence Checklists For Tech Teams - GainHQ: examines DORA metrics and the cost of hidden technical debt.